System and method of quantifying color and intensity of light sources

ABSTRACT

A system and method of quantifying color and intensity of light sources including LEDs, HBLEDs (High Brightness LEDs), and other Solid State Lights (SSLs) using C-parameters to model a Spectral Power Distribution (SPD) to improve precision, accuracy, repeatability and usefulness of measurement of optical properties of wavelength and radiant flux in manufacturing of an object, designing products and processes that use the object, and describing/defining the object, is provided. In one embodiment, a method of characterizing a Solid State Light (SSL) source includes a SSL source under test (DUT), a Spectral Power Distribution (SPD) of light emission of the SSL source, a curve-fitting function, a set of configuration data comprising the order of the curve-fitting function, the number of nodes, wavelength boundary limits, saturation threshold, and noise floor threshold, a computing device for curve-fitting, node detection, iteration and program control and inputting and outputting data; and a set of C-Parameters, noise parameters, and confidence values.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of application Ser. No. 13/205,170,filed Aug. 8, 2011, which is based upon and claims benefit of priorityfrom the Provisional patent application entitled “SYSTEM AND METHOD OFQUANTIFYING COLOR AND INTENSITY OF LIGHT”, Ser. No. 61/372,247, filedAug. 10, 2010, which are incorporated herein by reference in itsentirety.

FIELD OF THE INVENTION

The present invention relates generally to a system and method ofquantifying color and intensity of light sources, and more particularly,to a system and method of quantifying color and intensity of lightsources including LEDs (Light Emitting Diodes), HBLEDs (High BrightnessLEDs), and the other SSL (Solid State Light) sources using C-parametersto model a Spectral Power Distribution (SPD) to improve precision,accuracy, repeatability and usefulness of measurement of opticalproperties of wavelength and radiant flux in manufacturing of an object,designing products and processes that use the object, anddescribing/defining the object.

BACKGROUND OF THE INVENTION

Systems and methods used to quantify the attributes or performance of anobject play a crucial role in manufacturing the object, in designingproducts and processes that use the object, and in describing the objectto consumers.

Over the years, various lighting industries have developed a number ofsystems or methods for quantifying the color and intensity of a lightsource. Such systems and methods rely on metrics (systems of measurebased on a particular standard) and measurements (numerical valuesrepresenting an amount, extent, or size determined by measuring) thatare established by regulatory agencies, standards-producing bodies,industry stakeholders and individual organizations. The Metropolitan GasAct of 1860, for instance, quantified the intensity of a burning candleto a known standard, introducing the standard definition of the metric“candlepower”. In 1931, the International Commission on Illuminationintroduced the CIE 1931 XYZ Color Space and XYZ color coordinates. TheCIE 1931 color space created a metric for describing the perceived colorof an object based on a set of mathematical coordinates. The CIE 1931color metric is based on three visual response functions (a function isa relation between two sets in which one element of the second set isassigned to each element of the first set, as in the expression y=2x)describing the relation between color and intensity for the three typesof cone cells in the human eye. These are known as the color matchingfunctions and result in a color representation comprised of three values(a value is a particular magnitude, number, or amount) known astristimulus values. From the CIE tristimulus values metrics like colorcorrelated temperature (CCT), color rendering index (CRI), CIE (x,y),lumen, dominant wavelength and MacAdam ellipse may be measured orderived. These metrics, which quantify the appearance of lightingsystems to human observers under specified conditions, have been used bymanufacturers, designers and customers to grade products, calculate theperformance of the products in new applications, and compare productsfrom competing sources, enable manufacturers, designers and customers tograde products, calculate the performance of the products in newapplications, and compare products from competing sources.

The aforementioned and widely used luminous metrics are well suited forquantifying the color and intensity of an object under specificillumination and observing conditions by a human observer. A problemarises using these metrics for manufacturing SSLs and designing lightingsystems based on SSLs because there are many applications and processeswhere the SSL is not directly observed by the human eye. The presentinvention overcomes this problem of misapplication of metrics.

Furthermore, implicit assumptions in these metrics about the illuminant,field of view, ambient light, pupil dilation, and the relevance andaccuracy of the Color Matching Function (CMF) contribute errors whenthese metrics are used for many light sources, particularly LEDs, HBLEDsand the other SSL sources. The dominant wavelength and luminousintensity metrics assume a human observer in daylight is observing alight source through a restricted 2 or 10 degree field of view. Theseconditions are often not accurately reproduced during testing and arerarely appropriate to the manner in which light sources are actuallyviewed when assembled into a final product. These metrics suffer from aphenomenon known as metamerism which is the inability of a humanobserver to discern a certain mixture of different colored light sourcesfrom each other. All of these issues contribute uncertainty to themeasurement of spectral properties of SSLs adversely affecting precisionand repeatability of measurements. The present invention overcomes theselimitations.

These problems have less impact for lights producing a continuousspectrum (a classical black body emitter) such as the tungsten filamentfound in a traditional light bulb. However, traditional light color andintensity metrics have proven inadequate to quantify the color andintensity of SSL sources for design, manufacturing and assemblyprocesses. For example, SSL sources such as HBLEDs are used as theprimary source of light emission. Unlike a tungsten filament, HBLEDs arenot emitters of black body radiation. An LED radiates light by band-gapradiative recombination of electrons and holes in a compoundsemiconductor. The spectral characteristics of the emitted light from aSSL are significantly different from a black body radiation source.Characterizing the color and intensity of a SSL light source isfundamentally incorrect using the traditional metrics because theunderlying physics are fundamentally different. The present inventionovercomes the problem.

A typical manufacturing process for a SSL (hereinafter using LED as anexample) begins with the manufacturing of a LED on a wafer substrate.These substrates are inspected for physical and optical defects, and theSPD of LED emissions are recorded at various points on the wafer andconverted to metrics that are used to determine the uniformity andoptical characteristics of the wafer or die. Data collected during thisevaluation is commonly used in two ways. First, to control productquality, the data is compared against quality standards to determine howwell the wafer and its die meet quality standards. The quality of thewafer (determined by the number and nature of the defects and theoptical output) determines if the wafer is allowed to continue in themanufacturing process and determines the ultimate usability of thewafer. The second use of the data is for manufacturing processimprovements. The data collected during this evaluation is correlated tospecific process inputs. Once the correlation is determined, theseprocess inputs can be controlled and manipulated to improve processyield and reduce non-uniformities. The uncertainty of traditional lightmetrics used for SSLs and the unsuitability for use of the same insubsequent manufacturing process steps increase the range of variationof manufacturing processes. The present invention reduces this range ofvariation thereby leading to improvements in manufacturing processes ofSSLs and related lighting system design and manufacture.

SUMMARY OF THE INVENTION

The present invention relates generally to a system and method ofquantifying color and intensity of light sources, and more particularly,to a system and method of quantifying color and intensity of lightsources including LEDs, HBLEDs and other SSL sources using C-parametersto model a Spectral Power Distribution (SPD) to improve precision,accuracy, repeatability and usefulness of measurement of opticalproperties of wavelength and radiant flux in manufacturing of an object,designing products and processes that use the object, anddescribing/defining the object.

The present invention provides a C-Parameter system and method whichcomprises a system and method for describing color content and intensityof a light source, such as a LED, HBLED or other SSL source, withimproved precision and efficiency compared to the commonly used luminousmetrics which are well known to anyone skilled in the art. TheC-Parameter system and method is an improved system and method ofquantifying, specifying, communicating, evaluating, comparing andgrading the color and intensity of a light source, the sum of ordifference between light sources, and reflection and absorption of lightby reflective or refractive surfaces and materials. The C-Parametersystem and method is an improved system and method of controlling themanufacture of light sources, e.g. LEDs, HBLEDs, or other SSL lightsources.

The expression of the optical SPD as a series (a group of similar thingsarranged in order) of functions, one example being a Gaussian function,significantly improves the ability to quantify and characterize opticaloutput power of light sources, including SSL sources. The desired SPDcan be expressed concisely as a set of C-Parameters, and the quality ofa SSL being tested can then be described in terms of a set of deviationsor correlations of individual or subsets of C-Parameters, or as adeviation from or correlation with a desired SPD. A C-Parameter is areal number that is a coefficient of a function used to characterize theSPD of a SSL. A C-Parameter Tuple is a set of C-Parameters that togethersatisfy the solution of a function used to characterize the SPD of aSSL. The term C-Parameters denotes the set of C-Parameter Tuples that,when combined, characterize the SPD of a SSL.

BRIEF DESCRIPTION OF THE DRAWINGS OF THE INVENTION

FIG. 1 is a diagram illustrating one embodiment of a C-Parameter systemand method of quantifying color and intensity of a SSL source, inaccordance with the principles of the present invention.

FIG. 2 is a diagram illustrating one embodiment of a method of findingnodes in a SPD, in accordance with the principles of the presentinvention.

FIG. 3 is a diagram illustrating one embodiment of a process ofinputting the C-Parameters and outputting an SPD and various metricsusing the C-Parameter system and method of quantifying color andintensity of a SSL source, in accordance with the principles of thepresent invention.

FIG. 4 is a diagram illustrating one embodiment of a SSL manufacturingtester apparatus for illumination devices. The apparatus implements theC-Parameter system and method of quantifying color and intensity of aSSL source, in accordance with the principles of the present invention.

FIG. 5 is a diagram illustrating one embodiment of a SSL manufacturingtester apparatus for biomedical devices that implements the C-Parametersystem and method of quantifying color and intensity of a SSL source, inaccordance with the principles of the present invention.

FIG. 6 is a diagram illustrating one embodiment of a manufacturing SSLtester apparatus in a networked C-Parameter module configuration thatimplements the C-Parameter system and method of quantifying color andintensity of a SSL source, in accordance with the principles of thepresent invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT OF THE INVENTION

FIG. 1 illustrates one embodiment of the present invention in which aSSL source, such as a HBLED device under test 100 (hereinafter referringto “DUT 100”), emits light energy 102 into a spectral acquisition testapparatus. 104 which captures the emitted light energy as a spectrumwhich describes power or energy as a function of wavelength. For thepurposes of HBLED test and measurement this spectrum is an unfilteredspectral power distribution (SPD) 106. The unfiltered SPD 106 is inputto the C-Parameter module 108. The C-Parameter module 108 characterizesthe energy or power emitted by the DUT 100 as a function of wavelengthin terms of a linear superposition of functions. The specific form ofthe function used in the series is determined by the physics of the DUT100. In the instance of the HBLED application, the physical mechanisms(hole/electron radiative recombination in a band-gap compoundsemiconductor) that cause optical emissions are describable by afunction known as a Gaussian function. In this instantiation of thepresent invention, the C-Parameter module 108 is arranged and configuredto solve for Gaussian function coefficients. Each arrangement andconfiguration provides coefficients to a Gaussian function. As such, forthis instantiation of the present invention, the C-Parameter module 108outputs C-Parameters 146 comprising a plurality of sets of 3coefficients, each set determining one Gaussian curve referred to as anode 124. The C-Parameters associated with a given SPD 112 are thecoefficients of the series of Gaussian curves referring to as a set ofnodes 124 such that, when summed, the resulting curve has an effectivecorrelation to the shape and magnitude of the measured SPD 112.

Further in FIG. 1, a Spectral Selection process 110 takes as inputWavelength Thresholds (high and low) 114, Signal to Noise (S/N)Threshold 116, and Stray Pixel Rejection Limit 118 to select spectraldata of interest from the unfiltered SPD 106 and outputs the thresholdinputs 114, 116, 118 and the selected SPD 112 to a Node Finder process120.

The Node Finder process 120 then takes as input number of nodes 126 tofind the SPD 122 and outputs the SPD 122 as well as each “found” node124, with node boundaries 132 to a Node Classifier process 130.

The Node Classifier process 130 then determines the optimized order 1 .. . N 134 for each node 124. The node boundaries 132 and order 1 . . . N134 are used as constraints by the Initial Solver process 136.

The Initial Solver process 136 iterates over each node 124 and eachorder 1 . . . N 134 using the node boundaries 132 to determine aninitial solution set of initial C-Parameters 138 and initial NoiseParameters 140 for each node 124 and order 1 . . . N 134.

The initial C-Parameters 138 and initial Noise Parameters 140 are inputsto a Solver process 142 which finds coefficients of the best fitsolution of the Gaussian function for each order 1 . . . N 134 boundednode (124, 132) and outputs them as a set of N-order C-Parameters 146together with a Confidence Factor 144 and Noise Parameters 150.

Further in FIG. 1, a set of Normalized C-Parameters 148 may beoptionally produced. The Normalized C-Parameters 148 are C-Parameterswhere the magnitude of the sum of the individual curves has beennormalized to remove the influence of the intensity, while preservingthe relative color content or wavelength.

It is appreciated that C-Parameters are a set of real numbers whichprecisely describe the optical output power of a light source as afunction of wavelength (e.g. color content). The C-Parameter system andmethod models the optical output power of a light source as a set offunctions, for example, a series of Gaussian functions. In oneembodiment of the present invention wherein a Gaussian function is usedto model the optical output power of a SSL, there are 3 Gaussianfunction coefficients (hereinafter referred to as a C-Parameter tuple)that describe gain, center wavelength and standard deviationrespectively. In one embodiment of the present invention, whenconfigured for a 2^(nd) order fit, two C-Parameter tuples will begenerated, one for each order. Stated generally, an ‘n’ order fitproduces ‘n’ C-Parameter tuples. In one embodiment of the presentinvention, having 3 Gaussian function coefficients and a 2^(nd) orderfit, the method outputs 6 C-Parameters. In one embodiment of the presentinvention, for a SSL having two active light-emitting elements, such asa blue phosphor-pumped HBLED, the SSL SPD is modeled as a bimodaldistribution having two nodes. A node is an exclusive subset of the SPDcomprising all points within a minimum wavelength to a maximumwavelength. Functionally, each node corresponds to a light generatingmechanism within the DUT, for example: {Blue LED, Phosphor}, {Red LED,Green LED, Blue LED}, {Red LED, Green LED, Blue LED, Yellow LED}. In oneembodiment of the present invention, having two discrete emissionsources and thus two nodes, the method outputs 12 real numbers,organized into 2 sets (one per active element or SPD node) of 2C-Parameter tuples (one per order of the curve-fitting solution), eachC-Parameter tuple comprising 3 Gaussian function coefficients for atotal of 12 real numbered C-Parameters.

FIG. 2 is a graph of SPD outputs of various SSL sources, each onecomprising a blue HBLED device and a phosphor coating, showing thebimodal SPD that is produced. In one embodiment of the presentinvention, each SPD is modeled as having two Nodes 200, each Node 200bounded by a Minimum Wavelength 202 and a Maximum Wavelength 204 and fitwith a 2^(nd) order Gaussian function.

FIG. 3 illustrates one embodiment of the present invention in whichC-Parameters 146 for a device are used to output the SPD 122 as well asother metrics 302 that can be derived from the SPD 122 for that device.As shown, the problem of irreversibility of existing metrics is overcomeby reconstructing the SPD then deriving light output metrics from thereconstructed SPD. FIG. 3 also illustrates the method of compressing anSPD by encoding it as a set of C-Parameters which can be used touncompress the encoded information back into the original SPD withminimal loss of information. The amount of loss inversely correlates tothe order of the fitting function.

FIG. 4 illustrates one embodiment of the present invention in which aCUBE 5010CP system 400 implements the C-Parameter method to test a whiteHBLED SSL. In this embodiment, the DUT 100 is a white HBLED SSLcomprising 2 sources of light emission, a blue LED and a yellow phosphorcoating. A tester, for instance, a CUBE 5010 LED tester 404, inputselectrical energy 402 to the DUT 100. The DUT 100 emits light energy 102which is acquired by the tester 404 and passed to the CUBE 5010CP system400. The CUBE 5010CP system 400 comprises a C-Parameter Module 108 thatintegrates with the tester 404 to input light energy 102 and implementsthe C-Parameter method to output C-Parameters 146.

FIG. 5 illustrates one embodiment of the present invention in which aCUBE 5010CP system 500 implements the C-Parameter method to test ared/infrared SSL manufactured for biomedical applications. In thisembodiment, the DUT 100 is a red and infrared LED SSL comprising 2sources of light emission, a red LED and an infrared LED. A tester, onepossible instantiation being a CUBE 5010 LED tester 404, inputselectrical energy 402 to the DUT 100. The DUT 100 emits light energy 102which is acquired by the tester 404 and passed to the CUBE 5010CP system500. The CUBE 5010CP system 500 comprises a C-Parameter Module 108 thatintegrates with the tester 404 to input light energy 102 and implementsthe C-Parameter method to output C-Parameters 146.

FIG. 6 illustrates one embodiment of the present invention in which aCUBE 5010CP network-connected system 600 implements the C-Parametermethod to test a SSL in a network-connected configuration. In thisembodiment, the DUT 100 is a SSL. A tester, for instance, a CUBE 5010LED tester 404, inputs electrical energy 402 to the DUT 100. The DUT 100emits light energy 102 which is acquired by the tester 404 and passedover a network link 602 to the CUBE 5010CP network-connected system 600.The CUBE 5010CP network-connected system 600 comprises a C-ParameterModule 108 that integrates over a network connection with the tester 404to input the light energy data 102 and implements the C-Parameter methodto output C-Parameters 146.

The characteristics, features and advantages of the present inventionfor providing a C-Parameter system and method of quantifying color andintensity of SSL sources include, but not limited to, the following:

-   -   1. The C-Parameter system and method represents the nodes        present in an SPD as a set of curve-fitting function        coefficients.    -   2. One instantiation of the present invention yields        C-Parameters that represent one or more nodes present in an SPD        as a set of Gaussian function coefficients.    -   3. The C-Parameter system and method reconstructs an SPD from        the C-Parameters.    -   4. The C-Parameter system and method compresses an SPD without        loss of information.    -   5. The C-Parameter system and method characterizes an SPD of SSL        light sources, including but not limited to, LEDs.    -   6. The C-Parameter system and method normalizes the color        content of an SPD.    -   7. The C-Parameter system and method detects non-signal data        present in spectral measurements of LEDs such as, but not        limited to, configuration errors, measurement noise,        manufacturing defects.    -   8. The C-Parameter system and method filters noise that is        present in a measurement of an SPD of SSLs in such a way that it        improves noise filtering over traditional systems and methods        known to one skilled in the art such as box-car filtering or        Gaussian filtering.    -   9. The C-Parameter system and method removes dark noise and shot        noise present in spectrometric measurements of an SPD of LEDs        and increases the signal to noise ration of LEDs during testing.    -   10. The C-Parameter system and method produces metrics useful to        SSL manufacturing processes.    -   11. One instantiation of the present invention produces process        control data that improves the manufacturing process for        semiconductor LEDs used for SSL applications.    -   12. One instantiation of the present invention produces process        control data that improves the manufacturing process for        semiconductor LEDs used for biomedical applications.    -   13. The C-Parameter system and method improves binning, sorting        and grading of SSL devices during manufacture.    -   14. The C-Parameter system and method may be used to bin and        match LEDs for color mixing applications such as phosphor/blue        to optimize the combined output of an SSL.    -   15. The C-Parameter system and method is an improved measure of        the intensity of an LED than luminous flux.    -   16. The C-Parameter system and method is an improved measure of        the color of an LED than color coordinates in a color space.    -   17. One instantiation of the present invention improves optical        system design software over traditional techniques such as        N-band (RGB) representation.    -   18. C-Parameters provide color content information required to        accurately calculate optical interactions between an emitter and        mechanical, chemical and optical components of an LED or other        SSL device.    -   19. The C-Parameter system and method eliminates problems that        arise when using photometric descriptions for indirect lighting        applications involving reflected and absorbed light emissions.    -   20. The C-Parameter system and method accurately represents the        SPD of LEDs for applications other than direct observation by        human eyes. Examples of applications include photoactive organic        materials and human physiological responses to light such as        circadian rhythms and the stimulation by blue light of serotonin        and melatonin production.    -   21. The C-Parameter system and method is an improved system and        method of combining the SPDs of multiple LEDs into a single SPD.    -   22. The C-Parameter system and method isolates the SPD of each        emitter in a combined or composite power distribution such as        the emission of a white HBLED or an RGB white LED.    -   23. The C-Parameter system and method separately quantifies        color and intensity of LEDs as discrete elements.    -   24. The C-Parameter system and method improves over the “choose        the saddle” system and method to estimate junction temperature        of an LED.    -   25. The C-Parameter system and method identifies a separate        junction temperature curve for each LED in a composite emissive        device or SSL.

1-29. (canceled)
 30. A system of characterizing color of a Solid StateLight (SSL) source, comprising: an energy source for stimulating a SSLsource to emit light; a spectral acquisition apparatus for acquiring aSpectral Power Distribution (SPD) of light emitted by the SSL source; acomputing device, wherein the computing device comprises a C-parametermodule configured to: find a node in the SPD, wherein the node in theSPD comprises a continuous subset of the SPD having a predeterminedminimum and maximum wavelength, wherein the node corresponds to aspecific light generating mechanism; determine coefficients of one ormore Gaussian functions for curve fitting the node, wherein thecoefficients of the one or more Gaussian functions are expressed as aset of C-parameters; and output the set of C-Parameters that describesthe SPD of the SSL source.
 31. The system of claim 30, wherein thecoefficients of each of the one or more Gaussian functions and the setof C-Parameters comprise at least: a gain, a center wavelength, and astandard deviation.
 32. The system of claim 30, wherein the computingdevice is further configured to: reconstruct the SPD using the set ofC-Parameters by summing the one or more Gaussian functions as defined bythe set of C-parameters, wherein the reconstructed SPD has an effectiveshape and magnitude of the SPD.
 33. The system of claim 32, wherein thecomputing device is further configured to: generate a set of metricsderived from the reconstructed SPD.
 34. The system of claim 30, whereinthe computing device is further configured to: receive a number of nodesfound in the SPD and determine coefficients of one or more Gaussianfunctions for curve fitting each node of the number of nodes found inthe SPD.
 35. The system of claim 30, wherein the computing device isfurther configured to: optimize the coefficients of the one or moreGaussian functions for curve fitting the node, wherein a process ofoptimizing the coefficients of the one or more Gaussian functionsincludes obtaining a set of noise parameters and a set of confidencevalues.
 36. The system of claim 30, wherein the computing device isfurther configured to: normalize the set of C-Parameters to removeinfluence of intensity, while preserving relative color content andwavelength.
 37. The system of claim 30, wherein the computing device isfurther configured to: generate the set of C-Parameters for the SSLsource in a manufacturing process for the SSL source; and provideprocess control feedback data based on the set of C-Parameters forevaluation and improvement of the manufacturing process for the SSLsource.
 38. The system of claim 37, wherein the manufacturing processfor the SSL source includes one of binning, sorting, or grading of theSSL source.
 39. The system of claim 30, wherein the set of C-Parametersis used to compare color and intensity of the SSL source with a secondSSL source.
 40. The system of claim 30, wherein the computing device isfurther configured to: bin the SSL source using the set of C-parameters.41. The system of claim 30, wherein the specific light generatingmechanism is one selected from the following: phosphor light generatingmechanism, recombination of electrons and holes having an energy bandgapfor generating blue light, recombination of electrons and holes havingan energy bandgap for generating green light, recombination of electronsand holes having an energy bandgap for generating yellow light,recombination of electrons and holes having an energy bandgap forgenerating red light, or recombination of electrons and holes having anenergy bandgap for generating infrared.
 42. The system of claim 30,wherein the SSL source is an infrared LED.
 43. The system of claim 30,wherein the set of C-Parameters is used to detect one of configurationerror, measurement noise, or manufacturing defect of the SSL.
 44. Thesystem of claim 30, wherein the set of C-parameters models the opticaloutput power of the SSL source as a series of Gaussian functions. 45.The system of claim 30, wherein the set of C-parameters is used todetect non-signal data present in the SPD.
 46. The system of claim 30,wherein the SPD includes a plurality of nodes, wherein each of theplurality of nodes comprises a contiguous subset of the SPD having apredetermined minimum and maximum wavelength, wherein each of theplurality of nodes corresponds to a specific light generating mechanism.47. The system of claim 30, wherein the computing device is furtherconfigured to: select a second node in the SPD; determine coefficientsof one or more Gaussian functions for curve fitting to the second nodeof the SPD, wherein the coefficients of the one or more Gaussianfunctions are expressed as a second set of C-parameters; and output thesecond set of C-Parameters.
 48. The system of claim 30, wherein the setof C-parameters encodes the SPD using a finite set of real numbers. 49.The system of claim 30, wherein the computing device is furtherconfigured to: determine an order for the node, wherein the orderdefines a number of Gaussians to use in the series of Gaussians to fitthe node of the SPD, wherein the order also determines a number of setsof C-parameters to produce.
 50. A method of characterizing color of aSolid State Light (SSL) source, comprising: stimulating a SSL source toemit light using an energy source; acquiring a Spectral PowerDistribution (SPD) of light emitted by the SSL source using a spectralacquisition apparatus; finding, using a computing device, a node in theSPD, wherein the node in the SPD comprises a continuous subset of theSPD having a predetermined minimum and maximum wavelength, wherein thenode corresponds to a specific light generating mechanism; determiningcoefficients of one or more Gaussian functions for curve fitting thenode in the SPD, wherein the coefficients of the one or more Gaussianfunctions are expressed as a set of C-Parameters; and outputting the setof C-Parameters that describes the SPD of the SSL source.